Bayesian inference on modern complex simulator models is in general a difficult task because the likelihood function of the model's output is often difficult or impossible to obtain. A modern approach for bypassing such an obstacle is to use likelihood-free inference methods, in which a flexible function (e.g. a neural network) learns how to approximate the posterior distribution based on simulations over different parameters. This approach is also called simulation-based inference (SBI) and we refer the reader to [1]} for a review on the topic and the Python package sbi for ready-to-use implementations [2]}.
